多端口散射器负载预测的聚类-预测框架调整
Adjustment of Cluster-Then-Predict Framework for Multiport Scatterer Load Prediction
AI总结：
本文提出一种两阶段聚类-预测框架，用于多端口散射器的多负载值预测，通过捕捉S参数与负载阻抗之间的关系，将RMSE降低46%，并引入RUI指标评估性能。
摘要：
预测多端口散射器中相互依赖的负载值具有挑战性，因为存在高维性和阻抗与散射能力之间的复杂依赖关系。本文提出了一种两阶段聚类-预测框架，用于多负载值预测任务。所提出的聚类-预测方法有效捕捉了S参数与相应负载阻抗之间的潜在函数关系，在应用于梯度提升（GB）时，将RMSE降低至基线的46%。这一改进在各种聚类和回归方法中保持一致。此外，我们引入了现实统一指数（RUI），作为衡量多个具有冲突目标和不同尺度的指标之间权衡的量化分析指标，适用于现实场景中的性能评估。基于RUI，K-means聚类与k最近邻（KNN）的组合被确定为分析多端口散射器的最佳设置。
英文摘要：
Predicting interdependent load values in multiport scatterers is challenging due to high dimensionality and complex dependence between impedance and scattering ability, yet this prediction remains crucial for the design of communication and measurement systems. In this paper, we propose a two-stage cluster-then-predict framework for multiple load values prediction task in multiport scatterers. The proposed cluster-then-predict approach effectively captures the underlying functional relation between S-parameters and corresponding load impedances, achieving up to a 46% reduction in Root Mean Square Error (RMSE) compared to the baseline when applied to gradient boosting (GB). This improvement is consistent across various clustering and regression methods. Furthermore, we introduce the Real-world Unified Index (RUI), a metric for quantitative analysis of trade-offs among multiple metrics with conflicting objectives and different scales, suitable for performance assessment in realistic scenarios. Based on RUI, the combination of K-means clustering and k-nearest neighbors (KNN) is identified as the optimal setup for the analyzed multiport scatterer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
